Diabetes mellitus is a chronic metabolic disorder caused by an inability of the pancreas to produce sufficient amounts of the hormone insulin, resulting in the decreased ability of the body to metabolize glucose. This failure leads to hyperglycemia, i.e. the presence of an excessive amount of glucose in the blood plasma. Persistent hyperglycemia and/or hypoinsulinemia has been associated with a variety of serious symptoms and life threatening long term complications such as dehydration, ketoacidosis, diabetic coma, cardiovascular diseases, chronic renal failure, retinal damage and nerve damages with the risk of amputation of extremities. Because restoration of endogenous insulin production is not yet possible, a permanent therapy is necessary which provides constant glycemic control in order to always maintain the level of blood glucose within normal limits. Such glycemic control is achieved by regularly supplying external insulin to the body of the patient to thereby reduce the elevated levels of blood glucose.
External biologic such as insulin was commonly administered by means of multiple daily injections of a mixture of rapid and intermediate acting drugs via a hypodermic syringe. It has been found that the degree of glycemic control achievable in this way is suboptimal because the delivery is unlike physiological hormone production, according to which hormone enters the bloodstream at a lower rate and over a more extended period of time. Improved glycemic control may be achieved by the so-called intensive hormone therapy which is based on multiple daily injections, including one or two injections per day of a long acting hormone for providing basal hormone and additional injections of a rapidly acting hormone before each meal in an amount proportional to the size of the meal. Although traditional syringes have at least partly been replaced by insulin pens, the frequent injections are nevertheless very inconvenient for the patient, particularly those who are incapable of reliably self-administering injections.
Substantial improvements in diabetes therapy have been achieved by the development of the drug delivery device, relieving the patient of the need for syringes or drug pens and the administration of multiple daily injections. The drug delivery device allows for the delivery of drug in a manner that bears greater similarity to the naturally occurring physiological processes and can be controlled to follow standard or individually modified protocols to give the patient better glycemic control.
In addition, delivery directly into the intraperitoneal space or intravenously can be achieved by drug delivery devices. Drug delivery devices can be constructed as an implantable device for subcutaneous arrangement or can be constructed as an external device with an infusion set for subcutaneous infusion to the patient via the transcutaneous insertion of a catheter, cannula or a transdermal drug transport such as through a patch. External drug delivery devices are mounted on clothing, hidden beneath or inside clothing, or mounted on the body and are generally controlled via a user interface built-in to the device or on a separate remote device.
Blood or interstitial glucose monitoring is required to achieve acceptable glycemic control. For example, delivery of suitable amounts of insulin by the drug delivery device requires that the patient frequently determines his or her blood glucose level and manually inputs this value into a user interface for the external pumps, which then calculates a suitable modification to the default or currently in-use insulin delivery protocol, i.e. dosage and timing, and subsequently communicates with the drug delivery device to adjust its operation accordingly. The determination of blood glucose concentration is typically performed by means of an episodic measuring device such as a hand-held electronic meter which receives blood samples via enzyme-based test strips and calculates the blood glucose value based on the enzymatic reaction.
Continuous glucose monitoring (CGM) has also been utilized over the last twenty years with drug delivery devices to allow for closed loop control of the insulin(s) being infused into the diabetic patients. To allow for closed-loop control of the infused insulins, proportional-integral-derivative (“PID”) controllers have been utilized with mathematical model of the metabolic interactions between glucose and insulin in a person. The PID controllers can be tuned based on simple rules of the metabolic models. However, when the PID controllers are tuned or configured to aggressively regulate the blood glucose levels of a subject, overshooting of the set level can occur, which is often followed by oscillations, which is highly undesirable in the context of regulation of blood glucose.
Alternative controllers were also investigated. It was determined that a model predictive controller (“MPC”) used in the petrochemical industries where processes involved large time delays and system responses, was the most suitable for the complex interplay between insulin, glucagon, and blood glucose. The MPC controller has been demonstrated to be more robust than PID because MPC considers the near future effects of control changes and constraints in determining the output of the MPC whereas PID typically involves only past outputs in determining future changes. Constraints can be implemented in the MPC controller such that MPC prevents the system from running away when the limit has already been reached. Another benefit of MPC controllers is that the model in the MPC can, in some cases, theoretically compensate for dynamic system changes whereas a feedback control, such as PID control, such dynamic compensation would not be possible.
MPC can be viewed therefore as a combination of feedback and feedforward control. MPC, however, typically requires a metabolic model to mimic as closely as possible to the interaction between insulin and glucose in a biological system. As such, due to person-to-person biological variations, MPC models continue to be further refined and developed and details of the MPC controllers, variations on the MPC and mathematical models representing the complex interaction of glucose and insulin are shown and described in the following documents:    U.S. Pat. No. 7,060,059; US Patent Application Nos. 2011/0313680 and 2011/0257627; International Publication WO 2012/051344;    Percival et al., “Closed-Loop Control and Advisory Mode Evaluation of an Artificial Pancreatic β Cell: Use of Proportional-Integral-Derivative Equivalent Model-Based Controllers” Journal of Diabetes Science and Technology, Vol. 2, Issue 4, July 2008.    Paola Soru et al., “MPC Based Artificial Pancreas; Strategies for Individualization and Meal Compensation” Annual Reviews in Control 36, p. 118-128 (2012),    Cobelli et al., “Artificial Pancreas: Past, Present, Future” Diabetes Vol. 60, November 2011;    Magni et al., “Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial” Journal of Diabetes Science and Technology, Vol. 3, Issue 5, September 2009.    Lee et al., “A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator” Journal of Diabetes Science and Technology, Vol. 3, Issue 5, September 2009;    Lee et al., “A Closed-Loop Artificial Pancreas based on MPC: Human Friendly Identification and Automatic Meal Disturbance Rejection” Proceedings of the 17th World Congress, The International Federation of Automatic Control, Seoul Korea Jul. 6-11, 2008;    Magni et al., “Model Predictive Control of Type 1 Diabetes: An in Silico Trial” Journal of Diabetes Science and Technology, Vol. 1, Issue 6, November 2007;    Wang et al., “Automatic Bolus and Adaptive Basal Algorithm for the Artificial Pancreatic β-Cell” Diabetes Technology and Therapeutics, Vol. 12, No. 11, 2010; and    Percival et al., “Closed-Loop Control of an Artificial Pancreatic β-Cell Using Multi-Parametric Model Predictive Control” Diabetes Research 2008.    Kovatchev et al., “Control to Range for Diabetes: Functionality and Modular Architecture” Journal of Diabetes Science and Technology, Vol. 3, Issue 5, September 2009 (hereafter “Kovatchev”).    Atlas et al., “MD-Logic Artificial Pancreas System” Diabetes Care, Volume 33, Number 5, May 2010 (hereafter “Atlas”). All articles or documents cited in this application are hereby incorporated by reference into this application as if fully set forth herein.
In this field, it was determined by Kovatchev in 2009 that a control to range, i.e., where the glucose value is maintained by the controller within a predetermined range of glucose values, was easier to implement in that the control to range only computes corrections with respect to the nominal open-loop strategy and no integral action is included while a simple linear model is used; constraints are not considered explicitly; and the aggressiveness of control actions is individualized. Thus, it is apparent that Kovatchev considers control-to-range (“CTR”) to be more desirable than a control-to-target (“CTT”) where the blood glucose was controlled to a fixed threshold. Despite this preference for CTR by Kovatchev, Atlas demonstrated in 2010 that a combination of CTR and CTT can be utilized to quite good efficacy in managing diabetes. Atlas, however, failed to describe or illustrate how the CTR and CTT were to be utilized in his experiments. Specifically, Atlas failed to show or describe the interplay between CTR and CTT and whether both CTR and CTT were utilized separately or concurrently.